Presentation on theme: "CITE EVIDENCE THAT ORGANISMS ARE LINKED BY LINES OF DESCENT FROM COMMON ANCESTRY LEARNING GOAL."— Presentation transcript:
CITE EVIDENCE THAT ORGANISMS ARE LINKED BY LINES OF DESCENT FROM COMMON ANCESTRY LEARNING GOAL
Organisms share many conserved core processes and features that evolved and are widely distributed among organisms today Structural and functional evidence supports the relatedness of all domains.
Examples of Such Evidence: DNA and RNA are carriers of genetic information in all life forms.
Major features of the genetic code are shared by all modern living systems. Same base pairs: A-T, G-C Same processes of reading the code and replicating it.
Metabolic pathways are conserved across all currently recognized domains. One such pathway is cellular respiration.
Structural evidence supports the relatedness of all eukaryotes. Eukaryotes are made up of eukaryotic cells which contain the following features: A cytoskeleton which is network of structural proteins that facilitate cell movement, morphological integrity (shape), and organelle transport.
ENDOMEMBRANE SYSTEM All eukaryotes have cells that contain an endomembrane system which is a collection of interrelated internal membranous sacs that divide a cell into functional and structural compartments.
Phylogenetic trees and cladograms can represent traits that are either derived or lost due to evolution. Phylogentic trees are branching diagrams that depict the evolutionary relationships of groups of organisms. domains of life
Cladograms are branching diagrams in which the endpoints of the branches represent different species of organisims. They are used to illustrate phylogenetic relationships.
Phylogenetic trees and cladograms illustrate speciation that has occurred. Relatedness of any two groups on the tree is shown by how recently two groups had a common ancestor. Can be constructed from morphological similarities of living or fossil species.
Phylogenetic trees and cladograms can also be constructed from DNA and protein sequence similarities by employing computer programs that have sophisticated ways of measuring and representing relatedness among organisms. They are dynamic (constantly being revised), based on the biological data used, new mathematical and computational ideas, and current and emerging knowledge.